Abstract
The recent progress in geographical information systems, remote sensing (RS) and data analytics enables us to acquire and process large amount of Earth observation data. Convolutional neural networks (CNN) are being used frequently in classification of multi-dimensional images with high accuracy. In this paper, we test CNNs for the classification of hyperspectral RS data. Our proposed CNN is a multi-layered neural network architecture, which is tailored to classify objects based on pixel-wise spatial information using spectral bands of hyperspectral imagery (HSI). We use benchmark satellite imagery in four different HSI datasets for classification using the proposed architecture. Our results are compared with support vector machine (SVM) and extreme learning machine (ELM) algorithms, which are frequently used techniques of machine learning in RS data classification. Moreover, we also provide a comparison with the state-of-the-art CNN approaches, which have been used for HSI classification. Our results show improvements of up to 6% on average over SVM and ELM while up to 4% improvement is observed in comparison with two recently proposed CNN architectures for HSI classification accuracy. On the other hand, the processing time of our proposed CNN is also significantly lower.
Zusammenfassung
Pixelweise Klassifizierung von Hyperspektralszenen mit Convolutional Neural Networks. Der Fortschritt bei Geoinformationssystemen, Fernerkundung und Datenanalyse erlaubt uns die Gewinnung und Verarbeitung von umfangreichen Erdbeobachtungdaten. Convolutional Neural Networks (CNN) werden oft zur Klassifizierung von multidimensionalen hoch aufgelösten Bilddaten verwendet. In diesem Artikel untersuchen wir die Eignung von CNNs für die Klassifizierung von hyperspektralen Fernerkundungsdaten. Das von uns vorgeschlagene CNN besitzt die Struktur eines neuronalen Netzwerks mit mehreren Ebenen zur Objekt-Klassifizierung auf der Grundlage einer pixelweisen Auswertung der hyperspektralen Bilddaten. Zur Verifizierung unserer Klassifizierungsmethode benutzen wir vier verschiedene Datensätze, aufgenommen von Satellitenplattformen. Die Ergebnisse werden mit denen der Methoden Support Vector Machine (SVM) und Extreme Learning Machine (ELM), die beide bei automatischen Klassifizierungsverfahren der Fernerkundung weit verbreitet sind, verglichen. Darüber hinaus liefern wir einen Vergleich zu aktuellen Ansätzen der CNN. Unsere Ergebnisse zeigen eine Verbesserung der Klassifizierungsgenauigkeit von 6% gegenüber SVM und ELM sowie eine Verbesserung von 4% gegenüber kürzlich veröffentlichen CNN-Architekturen. Darüber hinaus ist unser Ansatz deutlich schneller.
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Hussain, S.A., Tahir, A., Khan, J.A. et al. Pixel-Based Classification of Hyperspectral Images Using Convolutional Neural Networks. PFG 87, 33–45 (2019). https://doi.org/10.1007/s41064-019-00066-z
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DOI: https://doi.org/10.1007/s41064-019-00066-z